Supercharging Your Customer Acquisition Strategy

Chris George, Applied Data Science Manager, Civis Analytics
Civis Analytics
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Key Takeaways

What? Marketers are charged to continuously acquire more customers and expand into new and existing markets.

So what? Lookalike modeling is a common acquisition strategy, but a great alternative is to approach those who have expressed interest in your brand but don’t fall into your current customer base.

Now what? Identify and survey those that would be good targets for your business to tap into unidentified markets that are a potential great fit for your company.

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As a marketer, you are tasked with growing your customer base. This is a tall order for anyone, regardless if your brand or product has name recognition. No matter how big or small your current customer list, you may have a good idea of who your customers are (if you don’t, then that’s probably a good place to start)—but how do you go about finding more customers?

As a data scientist, the first option that often comes to mind is to use lookalike modeling, an exercise that identifies prospects that lookmost similar to your current customer base. (I won’t go into the details here as my colleague recently shared an example of how Lyric Opera was able to increase ticket buyers by 3.7 times using lookalike modeling over traditional sourcing methods.)

Instead, I’m going to focus on how marketers can reach a different target population: those who have expressed interest in your product or brand but don’t necessarily look like your current customer base or earliest adopters. This approach is a great way to uncover the right targets to expand to in both new and existing markets, whether geographically or demographically, that could include a host of new customers.

Finding these people might not seem like a difficult problem, but there’s quite a bit that goes into identifying and reaching those who have expressed interest.


Learn more from Civis Analytics to better understand how your costumers behave and how to find more like them.


First, how do you define and measure interest in a product or service? Simply asking a person their level of interest for a product, service or willingness to donate doesn’t quite pass the full test for a variety of reasons: People don’t always tell the truth; they may be unclear about what you are asking; or they might have biases based on past experiences, misinformation or a host of other things. While some of these people might not express interest in a product or service when asked, you shouldn’t be too quick to discount them as potential customers as some of their misconceptions can be changed with marketing and other advocacy tactics.

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We solve these problems with a complete data science workflow, identifying those that would be good targets for acquisition by surveying a sample of people who are within your organization’s market, yet are not part of your current user base. In our surveys, we ask respondents a variety of questions—some of which are about expressed interest in your product, service or organization—but others about their lifestyle, behaviors and preferences that you believe would deem them a good fit as a user of your product.

Each of these individual questions are designed to capture slightly different populations, as they measure different concepts of prospective behavior. The challenge then becomes how to combine these various measures into a single indicator of whether or not a person is a good acquisition target? We answer this question by building models for all of these measures as predictors, creating an acquisition prospect score that is then a composite measure for whether or not a customer would be a good fit. This new composite measure takes into account not only stated interest and demographic information of the consumer, but also behaviors and preferences that would lead them to be an ideal prospect for acquisition.

By using this method of customer acquisition modeling, you can unlock new opportunities for your business to expand and grow, rather than limiting marketing efforts to only consumers who look like your current user base or consumers who express interest in your product. Now, you can begin to reach past the low-hanging fruit to tap new markets of consumers who are great fits—they may just not know it yet.

For more information about Civis Analytics and its capabilities, please visit http://www.civisanalytics.com/services/, which includes more information about “lookalike,” attitudinal and channel-specific targeting methods.


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Author Bio:

https://auth.ama.org/PublishingImages/chris-george-civis.jpg
Chris George, Applied Data Science Manager, Civis Analytics
<p>As an Applied Data Science Manager, Chris leads a team of data scientists who design and implement unique data science solutions for clients, while managing day-to-day engagement communications. Chris has extensive experience in the end-to-end execution of client engagements – from data unification and enrichment to building predictive models and implementing insights into business processes. Before Civis, he served as the Modeling Data Manager for the 2012 Obama Analytics team. Chris earned a BS in Mathematics & Statistics from Miami University in Oxford, Ohio.</p>
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